Methods of assessing image quality are used in many different image processing applications. For example, image quality measurements may be used to optimize and automatically adjust algorithms and parameter settings in components of an image processing system, such as pre-processing and post-processing components of a camera or a video teleconferencing system. Image quality assessment methods also may be used to determine which of multiple imaging systems is best for a particular imaging task.
Although person-based subjective image quality assessment methods are used for some applications, these methods tend to be too slow and expensive for most applications. For this reason, objective image quality assessment methods have been developed to automatically predict human subjective assessments of image quality. Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) are commonly used in automatic objective image quality assessment approaches. In some approaches, a test video sequence is compared to a reference copy of the same sequence to assess the quality of the test video sequence. Such a reference-based approach often accurately and robustly predicts human subjective assessments of video quality. In many applications, however, a reference copy of the image or video sequence is not readily available for performing a comparative image quality assessment. For example, in consumer digital imaging applications, such as browsing and managing large image databases, key frame selection for video, and automatic photo layout design, reference images are not available.